Review
INMED/TINS special issue
Analysis of dynamic brain oscillations: methodological advances

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In recent years, new recording technologies have advanced such that, at high temporal and spatial resolutions, oscillations of neuronal networks can be identified from simultaneous, multisite recordings. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings also depends on the development of new mathematical methods that can extract meaningful information relating to time, frequency and space. Here, we aim to bridge this gap by focusing on up-to-date recording techniques for measurement of network oscillations and new analysis tools for their quantitative assessment. In particular, we emphasize how these methods can be applied, what property might be inferred from neuronal signals and potentially productive future directions. This review is part of the INMED and TINS special issue, Physiogenic and pathogenic oscillations: the beauty and the beast, derived from presentations at the annual INMED and TINS symposium (http://inmednet.com).

Section snippets

Introduction: the complex web of neuronal oscillations

Advances in our understanding of neural systems go hand-in-hand with improvements in experimental techniques used to study these systems. From the rapid growth in biotechnology, multisite recording techniques now enable monitoring of ensemble oscillations in great detail by simultaneously recording local neuronal activity from a large number of network locations [1] (Box 1). What emerges from these parallel neuronal recordings is a rich picture of brain dynamics, in which locally generated

Time–frequency structures of dynamic oscillations

The key to extracting information from a set of measurements is to display those measurements in another equivalent representation in which their information content becomes obvious. Often, the key to extracting information is to switch from a temporal domain to a frequency domain. The first such transformation in wide use is the Fourier transform, providing spectral power that identifies the amplitudes of sine functions of various frequencies that exist throughout the entire duration of the

Phase synchronization between neuronal oscillations

Simultaneous recording of multiple oscillations within and between different cortical regions offers insight into how distributed neuronal oscillations work together to generate complex brain functions. Recently, there has been a series of remarkable results showing that interaction dynamics between spatially distributed neuronal oscillations can be exploited for studying large-scale ‘functional integration’, that is, the transient integration of numerous neuronal ensembles that are widely

Ensemble synchronization: seeing both the forest and the trees

Most synchronization methods are defined for pairs of recording channels only. A global picture of synchronization in multichannel data can be obtained by averaging the pairwise synchronization between every possible pair of channels. The main problem with these pairwise correlations is their inability to detect ensemble synchronization over larger cortical areas because they are present in traveling oscillations. This is owing to the restriction of pairwise analysis and temporal averaging,

Causal networks of neuronal oscillations

Analysis of synchronization alone does not address the question of causality between two oscillations, the so-called ‘effective connectivity’ defining the influence one neuronal system exerts on another (‘who drives whom’) [46]. Traditional measures, such as cross-correlation, can, in principle, indicate the delay in coupling, but inferring causality from the time delay is not completely satisfactory [47]. Furthermore, two oscillations in a network do not have to interact directly. Therefore,

Cross-frequency coupling between oscillations

Investigating the interaction between different frequencies adds another dimension to the already complex identification of spatiotemporal and frequency-specific neuronal networks. In this context, direct cortical recordings reveal that cross-frequency couplings between distinct brain regions are abundant, most prominently as an interaction between low and high frequencies (e.g. Refs 58, 59, 60, 61, 62, 63), often mediating top-down modulating, ‘attentional’ or other context-defining functions 5

Concluding remarks

Here, we have outlined analysis protocols for oscillations in multichannel data. These tools have tremendous potential for studying oscillations in multisite recordings, enabling revelation of complex, dynamic relationships that cannot be derived from simple plots and statistics. In particular, these computational and analytical tools can be a powerful aid to making sense of the data, which, in turn, can influence the experimentation.

Nevertheless, it is not possible to develop ‘black box’-like

Acknowledgements

We thank G. Buzsaki, O. Paulsen, O. David, J.P. Lachaux and R. Staba for reading the article.

References (77)

  • P.J. Uhlhaas et al.

    Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology

    Neuron

    (2006)
  • T. Koenig

    Decreased EEG synchronization in Alzheimer's disease and mild cognitive impairment

    Neurobiol. Aging

    (2005)
  • F. Mormann

    Mean phase coherence as a measure for phase synchronization and its application to the EEG of epileptic patients

    Physica D

    (2000)
  • S. Boccaletti

    The synchronization of chaotic systems

    Phys. Rep.

    (2002)
  • J.R. Rosenberg

    Identification of patterns of neural connectivity – partial spectra, partial coherence, and neuronal interactions

    J. Neurosci. Methods

    (1998)
  • M. Winterhalder

    Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems

    Sig. Proc. J.

    (2005)
  • B. Schelter

    Testing for directed influences among neural signals using partial directed coherence

    J. Neurosci. Methods

    (2006)
  • C. Buchel et al.

    Assessing interactions among neuronal systems using functional neuroimaging

    Neural Netw.

    (2000)
  • G. Buzsáki

    Hippocampal network patterns of activity in the mouse

    Neuroscience

    (2003)
  • G. Dumermuth

    Analysis of interrelations between frequency bands of EEG by means of bispectrum. A preliminary study

    Electroencephalogr. Clin. Neurophysiol.

    (1971)
  • X. Li

    Interaction dynamics of neuronal oscillations analysed using wavelet transforms

    J. Neurosci. Methods

    (2007)
  • J.P. Lachaux

    A simple measure of correlation across time, frequency and space between continuous brain signals

    J. Neurosci. Methods

    (2003)
  • J.D. Kralik

    Techniques for long-term multisite neuronal ensemble recordings in behaving animals

    Methods

    (2001)
  • C.M. Gray

    Tetrodes markedly improve the reliability and yield of multiple single- unit isolation from multi-unit recordings in cat striate cortex

    J. Neurosci. Methods

    (1995)
  • J. Csicsvari

    Mechanisms of gamma oscillations in the hippocampus of the behaving rat

    Neuron

    (2003)
  • G. Buzsáki

    Large-scale recording of neuronal ensembles

    Nat. Neurosci.

    (2004)
  • G. Tononi et al.

    Consciousness and complexity

    Science

    (1998)
  • F. Varela

    The brainweb: phase synchronization and large-scale integration

    Nat. Rev. Neurosci.

    (2001)
  • G. Buzsaki et al.

    Neuronal oscillations in cortical networks

    Science

    (2004)
  • A.K. Engel

    Dynamic predictions: oscillations and synchrony in top-down processing

    Nat. Rev. Neurosci.

    (2001)
  • C. Torrence et al.

    A practical guide to wavelet analysis

    Bull. Am. Meteor. Soc.

    (1998)
  • I. Oren

    Synaptic currents in anatomically identified CA3 neurons during hippocampal gamma oscillations in vitro

    J. Neurosci.

    (2006)
  • C. Tallon-Baudry

    Stimulus specificity of phase locked and non-phase-locked 40 Hz visual responses in humans

    J. Neurosci.

    (1996)
  • J.B. Caplan

    Human theta oscillations related to sensorimotor integration and spatial learning

    J. Neurosci.

    (2003)
  • S. Blanco

    Time–frequency analysis of electroencephalogram series (III): wavelet packets and information cost function

    Phys. Rev. E

    (1998)
  • W.J. Williams

    Time-frequency analysis of electrophysiology signals in epilepsy

    IEEE Eng. Med. Biol. Mag.

    (1995)
  • Cited by (0)

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